An Artificial Intelligence‐Based Framework to Accelerate Data‐Driven Policies to Promote Solar Photovoltaics in Lisbon

Author:

Freitas Sara1ORCID,Silva Miguel1,Silva Eduardo1,Marceddu Alessandro2,Miccoli Massimo2,Gnatyuk Petro2,Marangoni Luca2,Amicone Alessandro2

Affiliation:

1. Lisboa E-Nova – Agência de Energia e Ambiente de Lisboa 1100-23 Lisbon Portugal

2. GFT Italia S.r.l. – Innovation Unit 20139 Milano Italy

Abstract

Due to the unavailability of up‐to‐date and georeferenced information about Lisbon's existing solar energy systems, tracking the progress of solar energy in relation to the city's Climate Action Plans 2030 is a complex task, thus hindering the potential of data‐driven decision‐making for a targeted implementation of photovoltaics (PV) in buildings and urban infrastructure. To overcome the challenges posed, an integrated approach to accelerate policy‐making based on artificial intelligence (AI) resources and local citizens' and stakeholders' participation is developed and piloted in Lisbon. Recurring to a two‐step AI model setup to identify and geolocate PV systems, key policy indicators are calculated to inform policy‐makers about the evolution of PV deployment in the city and contribute to tailor future incentives to more depressed or energy poor districts. The AI model based on open data orthophotos from 2016 allowed estimates for the installed peak power at the city level, in that year, to be delivered in a few minutes, whereas manual inspection of aerial images will have taken several months. Although the PV capacity determined is 30% lower than the historical official numbers, the proof of concept for the proposed framework is achieved and validated by local stakeholders.

Funder

Horizon 2020 Framework Programme

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference25 articles.

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2. Observatórios Lisboa https://observatorios-lisboa.pt/en/info_emissoes.html(accessed: August 2023).

3. Solis https://www.solis-lisboa.pt/mapa-solar-de-li/(accessed: August 2023).

4. Fundo Ambiental https://www.fundoambiental.pt/apoios-prr/c13-eficiencia-energetica-em-edificios/c13-i01-02-03-apoio-a-concretizacao-de-comunidades-de-energia-renovavel-e-autoconsumo-coletivo.aspx(accessed: August 2023).

5. AI4PublicPolicy https://ai4publicpolicy.eu/(accessed: August 2023).

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